Energy Efficient Geo-distributed Data Center Resizing Markov Chain Model

Abstract

Big data contains large-volume, complex and growing data sets with multiple, autonomous sources. Big data processing is the explosive growth of demands on computation, storage and communication in data centers, which hence incurs considerable operational expenditure to data center providers. Therefore, to minimize the cost is one of the issues for the upcoming big data era. Using these three factors, i.e., task assignment, data placement and data routing, deeply influenced by the operational expenditure of geo-distributed data centers. In this paper, we are ambitious to study the cost minimization problem via a joint optimization of these three factors for big data processing in geo-distributed data centers. Proposed using n-dimensional Markov chain and procure average task completion time.KeywordsBid dataDistributed data centerMinimize cost